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How to Pick Your First Data Science Software Package: A Comparison of R, Python, Stata and SPSS

This is the fourth and final article in our series from Data Science for Anyone that covers ways in which ordinary people can develop skills in data science to apply in their day job, advance in their career or succeed in data science courses. Don’t miss the second featured article in our series, which covers five tips to help you succeed in a career in data science, as well as our third featured article in our series, which explores our picks for the top 10 R packages for data science.

For anyone thinking about learning and developing skills in data science, there are many excellent software packages out there that provide a wide variety of tools to analyze, manage, examine and visualize data. As an aspiring data scientist, choosing which statistical software package to learn first can be a challenge, especially given the complexity and wealth of features provided by each offering on the market.

Here at Data Science for Anyone, we believe that each software package that you might use for data science projects offers unique features and functionalities with a wide variety of strengths and some potential shortcomings that are important to review.

If you’re just getting started in a career in data science or are thinking about pursuing this exciting profession, a frequent question that often comes up is: which data science software should I learn first?

Which data science software should I learn first?

Many experienced data scientists, programmers and statisticians would answer this question differently, so we decided to put together a detailed listing of the strengths, weaknesses and functionalities of R, Python, Stata and SPSS for data science in this article.

Please enjoy our in-depth look into these four popular data science software packages and feel free to share your thoughts in the comments below this article, such as your favorite software package, any other pros or cons your have noticed, or thoughts about other essential data science software programs.

Comparing the strengths and weaknesses of R, Python, SPSS and Stata

Statistical Software PackageStrengthsWeaknesses
R (the R Project for Statistical Computing)

-Extensive collection of data science tools included

-Free to download and use with all code open source

-RStudio is also free and provides a well-designed and full-featured IDE with point-and-click for some functionalities

-Thousands of user-written packages available to download from CRAN

-Frequently updated with new features, including the easy-to-use R markdown language

-Interfaces easily with numerous file formats and web apps using shiny

-Easy to import, export and manipulate files with a wide variety of data formats

-Can create numerous types of visualizations using ggplot from tidyverse

-Excellent help resources, including the acclaimed book, R for Data Science, which uses interesting examples and helps users master the tidyverse packages

-Very helpful R users online, including Stack Overflow and RStudio Communities
-Some packages and the R software itself can occasionally be buggy

-Lack of dedicated support due to free status

-RStudio Server is expensive

-Can be a bit slower to learn because it is all syntax-based


-Wide variety of data science tools available, based around 4 packages:
pandas, numpy, scipy and matplotlib

-Extensive algorithm development and machine learning capabilities, which are constantly being updated

-Free to download and use with open source code

-Many user-written packages to accomplish a wide variety of tasks, including data cleaning, transformation, analysis and visualization

-Intuitive IDEs with excellent usability, including Jupyer notebooks and Spyder IDE platform

-The Anaconda Navigator provides a user-friendly way to install, manage and run Python and other related data science programs, including RStudio, JupyterLab, Orange 3 and Qt console.

-Has functions to import and export files with a wide variety of data formats

-As a full-fledged programming language, Python can do much more than just data science tasks, including creating software programs, web apps and data dashboards

-Great network of Python programmers worldwide, who are very helpful on Stack Overflow
-Considerable learning curve, especially for those from a non-programming background

-Getting packages loaded can sometimes be buggy (pip install helps this issue)

-Loading some data formats can be difficult, yet extensive

SPSS (Statistical Package for the Social Sciences)

-Very user-friendly and easy to learn, with both point-and-click interface and optional use of syntax code

-Many functions for descriptive and inferential statistics, as well as plotting, graphs and visualizations

-Can easily import and export files with a wide variety of data formats

-Has many different customization options for figures and can create quality graphs and plots quickly

-Consistent and helpful support from IBM, who developed SPSS and upgrades the major versions of the software yearly or every two years, with semi-frequent patches

Cost can range from $75 to several hundred dollars, with more expensive options for business clients

-Somewhat difficult to work with many objects and data frames at one time, due to single dataset-focused interface

-Limited customization functionality for output

-Lack of options to export custom formatted tables and figures

-Very few user-written packages and advanced functions to conduct more recently developed machine learning algorithms


-Wide variety of data science tools to conduct many analyses, including descriptive and inferential statistics, as well as visualization and plotting tools

-Extensive community of Stata users online, who post and answer questions on the Statalist message board

-Detailed and intuitive manuals for each command created by the Stata Corp., who also provides professional product support

-New features added with every official release and frequent patches/minor updates

-Excellent user-written and expert reviewed packages available for more specialized tasks

-Capabilities to work with many data types and can easily export datasets to other formats

-Has both point-and-click and syntax based commands, which can be easier to learn for some users
Cost can be several hundred dollars, depending on version

-Cheaper versions are capped at certain number of observations and variables

-Fewer user-written packages as compared to R or Python

-Official commands included with Stata can sometimes be limited in custom options

-Harder to work with many data frames and objects at one time, due to single dataset-focused interface

The bottom line: So which software package is best for data science? R, Python, SPSS or Stata?

R, Python, SPSS and Stata each have their own unique strengths and weaknesses, yet one recent trend in data science software is undeniable. Due to their free and open-source status, both R and Python have seen their popularity grow dramatically in recent years, far outpacing the growth of SPSS, Stata and even SAS (a popular data science package in health care and business).

In fact, this exceptional article on the top data science packages based on demand among employers, shows that Python is the #1 and R is the #5 ranked data science software package based on qualifications desired by companies hiring for data science positions. For those who are curious, #2 was SQL, #3 was Java and #4 was Amazon’s ML machine learning suite. Interestingly, SPSS and Stata didn’t even rank in the top 10!

It will remain to be seen whether R continues its rise to the top and whether Python can remain as the #1 data science software in years to come. It will also be very interesting to see how data science and statistics education programs at colleges, universities and online might change their curriculum in the coming years to focus mostly on R and Python.

Now that you have learned about the strengths and weaknesses of these four data science and statistical software packages, get started using them by following our tutorial on installing R, Python, SPSS and Stata and don’t forget to check out our easy-to-understand, step-by-step guide to running basic statistics in each program. If you are looking for a great way to start your learning off right, our reviews of books on data science and statistics are another great first step.

If you want to more in-depth look at the specific functionalities provided by R, SPSS, Stata and SAS, check out this website that lists the analysis methods available in each program, ranging from Chi-square tests and bivariate correlations to artificial neural networks and Hidden Markov Models.

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